Computational evidence for hierarchically structured reinforcement learning in humans.

ABSTRACT: Humans have the fascinating ability to achieve goals in a complex and constantly changing world, still surpassing modern machine-learning algorithms in terms of flexibility and learning speed. It is generally accepted that a crucial factor for this ability is the use of abstract, hierarchical representations, which employ structure in the environment to guide learning and decision making. Nevertheless, how we create and use these hierarchical representations is poorly understood. This study presents evidence that human behavior can be characterized as hierarchical reinforcement learning (RL). We designed an experiment to test specific predictions of hierarchical RL using a series of subtasks in the realm of context-based learning and observed several behavioral markers of hierarchical RL,

SUBMITTER: Eckstein MK 

PROVIDER: S-EPMC7703642 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature